LOW COST IoT SYSTEM FOR THE ASSET CONTROL SUPPORT BASED ON BARCODE SCANNING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose. The goal of the paper is to describe analysis and implementation IoT system for support the asset control via barcode scanning. Originality. The paper deals with the research on surveys for development an IoT device for searching correct store location of the devices in the laboratory and support asset checking for selected location. Methodology. The paper proposes one of the possibilities for development an IoT device basing on ESP8266 using Nextion intelligent display and a Windows application developed using C#. Retrieving data from a remote database, the application updates the data from central server. Authors described the whole development process starting from computer design of the proposed IoT device, chose the elements for hardware unit, design and implementation the Windows application and also experimental verification of derived results. Result. In this work authors proposed experimental sample of IoT system for the asset control via barcode scanning. The client-server application was designed to support the control of property records with the design of IoT equipment. The design of IoT devices is realized by modular connection of components. By implementing the GUI on the display, it is possible to control the reader module and observe the records in the informative mode and the control mode. 3D models are a device in a housing, where the output is a display. Using developed application, it is possible to connect to an IoT device and perform asset registration control by communicating with each other. This system implements all theoretical results described in the paper, and confirms them basing on the experiments provided. Practical value. Proposed IoT system could be practically used in university laboratories to control equipment location at any moment of time. References 11, figures 14.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it